Machine-learning-aided prediction of cancer attributed mortality using natural radiation, major air pollutants, and temperature as influencing variables.
Aim: Air pollution, radiation, and temperature have been linked with cancer mortality, but studies that used integrated machine learning and geographic information systems to predict it are limited. The aim of this study is to explore machine-learning models and geographic information systems to pre...
| Publicado en: | Journal of Public Health: From Theory to Practice (2198-1833) Vol. 34; no. 4; pp. 865 - 888 |
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| Formato: | Artículo |
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Springer Nature
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192415240&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192415240 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 21981833 NENI jtl: Journal of Public Health: From Theory to Practice (2198-1833) issn: 21981833 maglogo: N pubinfo: dt: Apr2026 vid: 34 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 192415240 10.1007/s10389-024-02326-8 ppf: 865 ppct: 23 formats: tig: atl: Machine-learning-aided prediction of cancer attributed mortality using natural radiation, major air pollutants, and temperature as influencing variables. aug: au: Mogaraju, Jagadish Kumar affil: International Union for Conservation of Nature Commission On Ecosystem Management, 110001, New Delhi, India su: Mortality risk factors Air pollutants Risk assessment Random forest algorithms Boosting algorithms Prediction models Radiation Carbon Sulfur compounds Ultraviolet radiation Descriptive statistics Support vector machines Geographic information systems Environmental exposure Formaldehyde Carbon monoxide Methane Machine learning Tumors Temperature Nitrogen oxides Particulate matter Decision trees Data analysis software Disease complications sug: subj: Other basic organic chemical manufacturing All Other Basic Organic Chemical Manufacturing Conventional oil and gas extraction Other Basic Inorganic Chemical Manufacturing All other basic inorganic chemical manufacturing Mortality risk factors Air pollutants Risk assessment Random forest algorithms Boosting algorithms Prediction models Radiation Carbon Sulfur compounds Ultraviolet radiation Descriptive statistics Support vector machines Geographic information systems Environmental exposure Formaldehyde Carbon monoxide Methane Machine learning Tumors Temperature Nitrogen oxides Particulate matter Decision trees Data analysis software Disease complications keyword: Air pollution GIS Model validation Remote sensing Air pollution GIS Model validation Remote sensing ab: Aim: Air pollution, radiation, and temperature have been linked with cancer mortality, but studies that used integrated machine learning and geographic information systems to predict it are limited. The aim of this study is to explore machine-learning models and geographic information systems to predict cancer-attributed mortality (2020–2022) using air pollutants, radiation, and surface air temperature as independent variables. Furthermore, the model efficiencies were validated with geospatial inputs as background. Subject and methods: The datasets were collected from the National Cancer Registry Programme of the Indian Council of Medical Research and the National Aeronautics and Space Administration. Major air pollutants such as nitrogen dioxide, formaldehyde, black carbon, sulfur dioxide, particulate matter, carbon monoxide, methane, ultraviolet and short wave radiation, and surface air temperature were analyzed to examine their effect on cancer-attributed mortality for the study period 2020–2022. Machine-learning models and geospatial tools were used in this study. Results: Carbon monoxide, ultraviolet radiation, particulate matter, and surface air temperature were associated with cancer deaths during 2020–2022. Notably, the extra trees regressor model performed well with R values of 0.88 (2020), 0.83 (2021), and 0.83 (2022) respectively. A model validation framework was developed to evaluate prediction efficiencies when integrated machine learning and geospatial tools were used. Conclusion: Generally, air pollutants and surface air temperature were associated with cancer-attributed mortality during the study period. This highlights the importance of machine learning and geospatial tools with proper model validation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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